Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	130,764
2	South Dakota	126,561
3	Rhode Island	118,170
4	Utah	115,233
5	Arizona	111,818
6	Tennessee	110,921
7	Oklahoma	106,686
8	Iowa	106,248
9	Wisconsin	105,665
10	Arkansas	105,415
11	Nebraska	103,733
12	Kansas	101,618
13	Alabama	100,161
14	South Carolina	99,354
15	Mississippi	98,385
16	Indiana	98,382
17	Idaho	95,539
18	Nevada	95,028
19	Wyoming	93,652
20	Illinois	93,465
21	Montana	93,158
22	Louisiana	91,999
23	Georgia	91,640
24	Texas	90,816
25	Kentucky	90,793
26	California	89,951
27	Delaware	88,112
28	Florida	88,104
29	New Mexico	87,789
30	New Jersey	87,699
31	Minnesota	85,448
32	Missouri	84,043
33	Massachusetts	83,568
34	New York	83,284
35	Ohio	82,333
36	North Carolina	81,764
37	Alaska	78,922
38	Connecticut	78,299
39	Colorado	74,234
40	West Virginia	72,992
41	Pennsylvania	72,491
42	Virginia	66,894
43	Michigan	64,361
44	Maryland	62,796
45	District of Columbia	56,850
46	New Hampshire	54,841
47	Washington	44,780
48	Puerto Rico	41,717
49	Oregon	36,650
50	Maine	32,819
51	Vermont	23,782
52	Hawaii	19,243

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	385
2	Rhode Island	379
3	New York	376
4	New Jersey	369
5	Connecticut	357
6	Georgia	338
7	Kentucky	302
8	Florida	300
9	Texas	295
10	Massachusetts	263
11	Arkansas	261
12	North Carolina	261
13	Utah	245
14	Alabama	244
15	Delaware	243
16	South Dakota	237
17	Tennessee	228
18	Iowa	225
19	Virginia	223
20	New Hampshire	221
21	Louisiana	218
22	Mississippi	216
23	Pennsylvania	208
24	Ohio	200
25	Montana	198
26	Colorado	196
27	Oklahoma	194
28	Alaska	190
29	Idaho	188
30	Arizona	183
31	Nebraska	179
32	West Virginia	178
33	District of Columbia	173
34	Wyoming	173
35	New Mexico	168
36	Michigan	161
37	Nevada	161
38	California	149
39	Illinois	149
40	Wisconsin	144
41	Indiana	137
42	Maryland	137
43	North Dakota	135
44	Kansas	134
45	Minnesota	132
46	Maine	129
47	Vermont	123
48	Washington	117
49	Oregon	113
50	Missouri	108
51	Puerto Rico	37
52	Hawaii	28

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,601
2	New York	2,405
3	Rhode Island	2,356
4	Massachusetts	2,318
5	Mississippi	2,221
6	Arizona	2,173
7	Connecticut	2,135
8	South Dakota	2,116
9	Louisiana	2,056
10	Alabama	2,005
11	North Dakota	1,930
12	Pennsylvania	1,867
13	Indiana	1,855
14	Arkansas	1,788
15	Illinois	1,784
16	New Mexico	1,751
17	Iowa	1,723
18	Michigan	1,644
19	Tennessee	1,644
20	South Carolina	1,639
21	Kansas	1,621
22	Nevada	1,601
23	Georgia	1,569
24	Texas	1,495
25	Ohio	1,465
26	Delaware	1,443
27	District of Columbia	1,424
28	Florida	1,419
29	Missouri	1,361
30	California	1,300
31	Maryland	1,291
32	West Virginia	1,277
33	Montana	1,264
34	Wisconsin	1,201
35	Wyoming	1,159
36	Minnesota	1,155
37	Nebraska	1,128
38	Oklahoma	1,087
39	North Carolina	1,064
40	Kentucky	1,056
41	Colorado	1,045
42	Idaho	1,038
43	Virginia	932
44	New Hampshire	855
45	Washington	658
46	Puerto Rico	631
47	Utah	589
48	Oregon	524
49	Maine	521
50	Alaska	381
51	Vermont	325
52	Hawaii	305

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Virginia	18
2	Alabama	16
3	California	15
4	Arizona	14
5	Delaware	13
6	Georgia	10
7	Maine	10
8	Missouri	10
9	Kansas	9
10	Rhode Island	9
11	Texas	9
12	New Jersey	8
13	Oklahoma	8
14	Tennessee	8
15	Kentucky	7
16	Ohio	7
17	South Carolina	7
18	Florida	6
19	Iowa	6
20	Louisiana	6
21	Massachusetts	6
22	Mississippi	6
23	Pennsylvania	6
24	Wisconsin	6
25	Idaho	5
26	Nevada	5
27	New Mexico	5
28	New York	5
29	North Carolina	5
30	West Virginia	5
31	Wyoming	5
32	Connecticut	4
33	District of Columbia	4
34	Indiana	4
35	Maryland	4
36	Arkansas	3
37	Michigan	3
38	Oregon	3
39	Puerto Rico	3
40	South Dakota	3
41	Utah	3
42	Washington	3
43	Illinois	2
44	Montana	2
45	Nebraska	2
46	New Hampshire	2
47	Vermont	2
48	Colorado	1
49	Minnesota	1
50	North Dakota	1
51	Alaska	0
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	323,544	1	99
Chattahoochee	Georgia	280,004	2	99
Bent	Colorado	265,017	3	99
Lincoln	Arkansas	242,092	4	99
Dewey	South Dakota	239,308	5	99
Davidson	Tennessee	128,643	224	92
Richland	South Carolina	98,523	1105	64
York	South Carolina	93,783	1318	58
Orange	California	81,982	1864	40
Pierce	Washington	42,237	2901	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Emporia city	Virginia	7,108	3	99
Galax city	Virginia	6,932	4	99
Foard	Texas	6,926	5	99
Orange	California	1,225	1936	38
Davidson	Tennessee	1,223	1940	38
Richland	South Carolina	1,181	1989	36
York	South Carolina	1,157	2024	35
Pierce	Washington	602	2697	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.